Managing and mining large-scale networks (e.g., physical, biological, and social networks) is critical to a variety of application domains, ranging from personalized recommendation in social networks, to search for functional associations in biological pathways. Network linkage analysis can find a group of tightly connected people that form a community or discover the centrality of nodes such as hub and authority. Furthermore, advanced analysis of social networks may address very complicated mining tasks, such as evaluating the network value of customers and link prediction. Existing network analytical tools develop application-specific criteria to gauge the importance of nodes or to discover knowledge hidden in complex networks. However, there is a growing need to process standard queries efficiently in large-scale networks. An h-hop query that can be decomposed into an aggregation operation and a top-k operation cannot be answered easily by Structured Query Language (SQL) query engines. Moreover, the performance of using a relational query engine to process h-hop queries is often unacceptable. Thus, there exists a need to overcome at least one of the preceding deficiencies and limitations of the related art.